Marketing TechnologyData & AnalyticsHow to calculate customer lifetime value (CLTV): The complete guide

How to calculate customer lifetime value (CLTV): The complete guide

Forbes found that it costs 5x as much to attract a new customer as to retain an existing one. Here's everything you need to know about calculating CLTV.

Customer Lifetime Value (CLTV) allows marketers to predict how much revenue customers will generate for their business for the duration of their relationship.

It is increasingly a data-led estimation and a key metric of business health. CLTV helps marketers understand how much each individual customer is worth to them. It also shows how much can be spent to acquire new customers while ensuring return on investment (ROI).

Why should we calculate CLTV?

Businesses generally accept that retention of customers is more affordable than acquisition. Last year, Forbes reported that it cost five times as much to attract a customer compared to retaining an existing one.

But if a customer only ever intends to make a single purchase and never return, is it worth allocating as much marketing budget to them?

CLTV assists marketers in optimizing their acquisition and retention strategy.

Matthew Hull, Senior Business Intelligence Analyst at Fospha, says of its importance:

“It helps marketers allocate budget effectively to achieve a target volume of acquisitions, while maximizing return. It also allows them the ability to segment and target users based on their CLTV. For example, more targeting is required for users with a lower CLTV, to increase either the frequency or order value levels.”

“Additionally, it can help the business focus on targeting clusters of users that are likely to generate the highest CLTV, while putting less of a focus on acquiring ‘single purchase’ or ‘low LTV’ customers.”

A simple comparison

The LTV of two customers of an online shoe store might look like the following.

(HLTV = high lifetime value; LLTV = low lifetime value)

example chart showing how to calculate CLTV

In this instance, a high LTV customer might spend between $65 and $105 on shoes per year. A low LTV customer, on the other hand, may make a substantial purchase in the short term, but have no desire to buy shoes from this store again in the future.

We can see that after five years the high LTV customer proves to be profitable for the business. But the low LTV customer has cost more to market to than the net revenue generated from what they spent with the retailer. At least some of this marketing budget allocated to the low LTV customer, in this example, would likely have been better spent on acquisition of a new customer.

If we scale this up to account for hundreds or thousands of customers, we can see why CLTV can be so valuable to businesses.

How to calculate CLTV?

It is difficult to predict the exact future spending of a customer. But trustworthy data around user habits, savvy segmentation, and demographic statistics can all make for a worthwhile estimation.

Calculating the lifetime value of a customer can vary from business to business, as Hull highlights:

“This is arguably why it is not used more often. There is no standard definition for calculating CLTV. The variables included within the formula have historically differed by industry to suit the specific use case of the business.”

That said, according to Hull, there are two core components of the CLTV calculation. They are:

  1. Purchase Frequency – the frequency within a given period that customers purchase.
  2. Average Order Value – the average amount of revenue from each sale.

The simplest formula for calculating CLTV is:

Purchase Frequency x Average Order Value x Average Customer Lifespan

But it can be improved by accounting for initial acquisition costs and/or retention costs such as customer support or promotional discounts.

As Hull points out: “Beyond this, there are levels of complexity in the calculation which factor in the promotional value and margin within each sale.”

So a slightly more insightful CLTV calculation might be:

Purchase Frequency x Average Order Value x Average Customer Lifespan – (Acquisition Cost + Retention Cost)

Another input we often see in CLTV formulas is the Churn Rate. This is the percentage of customers who end their relationship with a company in a given period. It might be consistent across the average customer lifespan, or it might vary as time passes.

Figuring out your average customer lifespan

If you have access to it, good historical data of your customers’ activities is essential here. This allows you to, at first, view the average time between customer purchases. When looking at time between purchases, you can find the standard deviation for what the average time is.

If your customer then goes beyond two standard deviations (a typical measure of statistical significance) before making another purchase, we can assume the relationship is finished.

For new businesses, companies in certain industries do produce benchmarks which are useful if you don’t have the data to draw upon. Other marketers use one to three years as a ballpark. But we can generally regard anything beyond seven years as being too far in the future to be accurate.

What is Customer ROI?

As Hull highlights earlier, CLTV is useful for marketers who want to maximize return.

Customer ROI refers to the profit from each customer after any investment (marketing, acquisition, retention costs etc.) have been considered.

Using our shoe retailer example above, we can work out the ROI of the high LTV customer with the following formula:

Customer ROI = Total Spend – Marketing Spend

And with the figures from the table, that would come out to:

$295 = ($420 – $125)

How can you improve your CLTV calculation?

We’ve already touched upon how a comprehensive understanding of your customers will improve CLTV.

Unlike the shoe retailer example, any business just starting out on its CLTV journey is unlikely to have five years worth of sales data to analyze.

However, detailed, trustworthy data about a customer’s activities is the first step to ensuring the accuracy of CLTV calculation.

Analytics data which details customer orders – even from a relatively short time period – can give us the necessary insight into average order value (AOV) and purchase frequency. From there, we can then estimate the customer lifespan.

Beyond that, a data-driven multi-touch attribution model provides understanding of the touchpoints  that customers interact with before making a purchase. This ensures a more comprehensive view of the marketing channels that are impacting purchases and highlights what proportion of your budgets can be credited to individual touchpoints along the journey.

Customer journeys are increasingly complex and not all individuals interact with the same channels before they make a purchase.

With data-driven attribution, we might discover a frequent shopper on an ecommerce site who looks like a high LTV customer — who in reality interacts with far more marketing channels than an individual who might be deemed low LTV.

With this insight, a marketing campaign which targets the latter may be more successful for the business than one that targets the former.

How can you improve CLTV?

CLTV calculations are improved by solid data, but CLTV and customer loyalty are improved by putting in place a solid strategy.

Are your customers satisfied? Does your website offer a good user experience? Is your customer service efficient? Do your customers trust you with their data? Is public sentiment around your brand positive?

As your business grows and changes, all these questions need to be returned to. Improvements here can see increasing numbers of customers more likely to return, perhaps with added frequency and bigger purchases.

Calculating CLTV is an evolving task: Test, learn, and iterate

As your business evolves, customer habits change and data becomes ever more granular. Calculating CLTV needs to be an ongoing practice too.

With this in mind, understanding CLTV with as much accuracy as your data can give you is a valuable way to ensure your business is healthy and competitive into the future.

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